{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T19:19:41Z","timestamp":1777576781513,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61866002"],"award-info":[{"award-number":["61866002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["2020GXNSFDA297006"],"award-info":[{"award-number":["2020GXNSFDA297006"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["2018GXNSFAA138122"],"award-info":[{"award-number":["2018GXNSFAA138122"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["2015GXNSFAA139293"],"award-info":[{"award-number":["2015GXNSFAA139293"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Project of Guangxi Graduate Education","award":["YCSW2021311"],"award-info":[{"award-number":["YCSW2021311"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04316-3","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T11:53:46Z","timestamp":1669982026000},"page":"16226-16245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bio-inspired interactive feedback neural networks for edge detection"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1779-1753","authenticated-orcid":false,"given":"Chuan","family":"Lin","sequence":"first","affiliation":[]},{"given":"Yakun","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Yongcai","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,2]]},"reference":[{"key":"4316_CR1","doi-asserted-by":"publisher","first-page":"108026","DOI":"10.1016\/j.sigpro.2021.108026","volume":"183","author":"J Moon","year":"2021","unstructured":"Moon J, Hossain MB, Chon KH (2021) AR and ARMA model order selection for time-series modeling with ImageNet classification. Sig Process 183:108026","journal-title":"Sig Process"},{"key":"4316_CR2","doi-asserted-by":"publisher","first-page":"108030","DOI":"10.1016\/j.sigpro.2021.108030","volume":"183","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Guo X, Ren H et al (2021) Multi-view classification with semi-supervised learning for SAR target recognition. Sig Process 183:108030","journal-title":"Sig Process"},{"key":"4316_CR3","doi-asserted-by":"publisher","first-page":"108051","DOI":"10.1016\/j.sigpro.2021.108051","volume":"183","author":"Y Rao","year":"2021","unstructured":"Rao Y, Ni J, Xie H (2021) Multi-semantic CRF-based attention model for image forgery detection and localization. Sig Process 183:108051","journal-title":"Sig Process"},{"issue":"5","key":"4316_CR4","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbel\u00e1ez","year":"2011","unstructured":"Arbel\u00e1ez P, Maire M, Fowlkes C et al (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898\u2013916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"4316_CR5","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TPAMI.2014.2377715","volume":"37","author":"P Dollar","year":"2015","unstructured":"Dollar P, Zitnick CL (2015) Fast edge detection using structured forests. IEEE Trans Pattern Anal Mach Intell 37(8):1558\u20131570","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"4316_CR6","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","volume":"26","author":"DR Martin","year":"2004","unstructured":"Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530\u2013549","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4316_CR7","doi-asserted-by":"crossref","unstructured":"Lim JJ, Zitnick CL, Dollar P (2013) Sketch tokens: a learned mid-level representation for contour and object detection. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Portland,\u00a0pp 3158\u20133165","DOI":"10.1109\/CVPR.2013.406"},{"key":"4316_CR8","doi-asserted-by":"crossref","unstructured":"Xie S, Tu Z (2015) Holistically-nested edge detection. In:\u00a0Proceedings of the IEEE international comference on computer vision. Santiago,\u00a0pp 1395\u20131403","DOI":"10.1109\/ICCV.2015.164"},{"key":"4316_CR9","doi-asserted-by":"crossref","unstructured":"Liu Y, Cheng M-M, Hu X et al (2017) Richer convolutional features for edge detection. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu,\u00a0pp 3000\u20133009","DOI":"10.1109\/CVPR.2017.622"},{"key":"4316_CR10","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhao X, Huang K (2017) Deep crisp boundaries. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu,\u00a0pp 3892\u20133900","DOI":"10.1109\/CVPR.2017.187"},{"key":"4316_CR11","doi-asserted-by":"crossref","unstructured":"He J, Zhang S, Yang M et al (2019) Bi-directional cascade network for perceptual edge detection. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Long Beach,\u00a0pp 3828\u20133837","DOI":"10.1109\/CVPR.2019.00395"},{"key":"4316_CR12","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1109\/TMM.2020.2987685","volume":"23","author":"Y-J Cao","year":"2020","unstructured":"Cao Y-J, Lin C, Li Y-J (2020) Learning crisp boundaries using deep refinement network and adaptive weighting loss. IEEE Trans Multimedia 23:761\u2013771","journal-title":"IEEE Trans Multimedia"},{"key":"4316_CR13","doi-asserted-by":"crossref","unstructured":"Deng R, Liu S (2020) Deep structural contour detection. In:\u00a0Proceedings of the 28th ACM international conference on multimedia. Online, pp 304\u2013312","DOI":"10.1145\/3394171.3413750"},{"key":"4316_CR14","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.neucom.2020.06.069","volume":"409","author":"C Lin","year":"2020","unstructured":"Lin C, Cui L, Li F et al (2020) Lateral refinement network for contour detection. Neurocomputing 409:361\u2013371","journal-title":"Neurocomputing"},{"key":"4316_CR15","unstructured":"Szegedy C, Zaremba W, Sutskever I et al (2014) Intriguing properties of neural networks. In: 2nd International Conference on Learning Representations, ICLR 2014"},{"key":"4316_CR16","unstructured":"Athalye A, Engstrom L, Ilyas A et al (2018) Synthesizing robust adversarial examples. In: International conference on machine learning. PMLR, pp 284\u2013293"},{"key":"4316_CR17","doi-asserted-by":"crossref","unstructured":"Bashivan P, Kar K, DiCarlo JJ (2019) Neural population control via deep image synthesis.\u00a0Science 364(6439):eaav9436","DOI":"10.1126\/science.aav9436"},{"key":"4316_CR18","unstructured":"Schrimpf M, Kubilius J, Hong H et al (2020) Brain-score: which artificial neural network for object recognition is most brain-like?\u00a0BioRxiv, pp\u00a0407007"},{"key":"4316_CR19","unstructured":"Bear M, Connors B, Paradiso MA (2020)\u00a0Neuroscience: exploring the brain, enhanced edition: exploring the brain. Jones & Bartlett Learning,\u00a0Burlington"},{"issue":"1","key":"4316_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-020-9001-8","volume":"15","author":"W Hao","year":"2021","unstructured":"Hao W, Andolina IM, Wang W et al (2021) Biologically inspired visual computing: the state of the art. Front Comput Sci 15(1):1\u201315","journal-title":"Front Comput Sci"},{"issue":"1","key":"4316_CR21","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"4316_CR22","doi-asserted-by":"crossref","unstructured":"Yoo D, Park S, Lee J-Y et al (2015) Attentionnet: aggregating weak directions for accurate object detection. In:\u00a0Proceedings of the IEEE international conference on computer vision, pp 2659\u20132667","DOI":"10.1109\/ICCV.2015.305"},{"key":"4316_CR23","doi-asserted-by":"crossref","unstructured":"Ding J, Ye Z, Xu F et al (2022) Effects of top-down influence suppression on behavioral and V1 neuronal contrast sensitivity functions in cats. Iscience 25(1):103683","DOI":"10.1016\/j.isci.2021.103683"},{"key":"4316_CR24","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In:\u00a0International conference on representation learning. San Diego,\u00a0pp 1049\u20131556"},{"issue":"5","key":"4316_CR25","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez P, Maire M, Fowlkes C et al (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898\u2013916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4316_CR26","doi-asserted-by":"crossref","unstructured":"Silberman N, Hoiem D, Kohli P et al (2012) Indoor segmentation and support inference from rgbd images. In:\u00a0European conference on computer vision. Springer,\u00a0Florence,\u00a0pp 746\u2013760","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"4316_CR27","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.visres.2015.11.007","volume":"120","author":"DA M\u00e9ly","year":"2016","unstructured":"M\u00e9ly DA, Kim J, McGill M et al (2016) A systematic comparison between visual cues for boundary detection. Vision Res 120:93\u2013107","journal-title":"Vision Res"},{"issue":"5","key":"4316_CR28","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.1152\/jn.00384.2020","volume":"125","author":"B Wild","year":"2021","unstructured":"Wild B, Treue S (2021) Primate extrastriate cortical area MST: a gateway between sensation and cognition. J Neurophysiol 125(5):1851\u20131882","journal-title":"J Neurophysiol"},{"issue":"4","key":"4316_CR29","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1007\/s00429-021-02440-3","volume":"227","author":"C Fang","year":"2022","unstructured":"Fang C, Yan K, Liang C et al (2022) Function-specific projections from V2 to V4 in macaques. Brain Struct Function 227(4):1317\u20131330","journal-title":"Brain Struct Function"},{"key":"4316_CR30","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/0893-6080(95)00079-8","volume":"8","author":"S Grossberg","year":"1995","unstructured":"Grossberg S, Mingolla E, Williamson J (1995) Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation. Neural Netw 8:7\u20138","journal-title":"Neural Netw"},{"issue":"3","key":"4316_CR31","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/S0893-6080(98)00144-0","volume":"12","author":"E Mingolla","year":"1999","unstructured":"Mingolla E, Ross W, Grossberg S (1999) A neural network for enhancing boundaries and surfaces in synthetic aperture radar images. Neural Netw 12(3):499\u2013511","journal-title":"Neural Netw"},{"issue":"10\u201312","key":"4316_CR32","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1016\/j.neucom.2007.11.031","volume":"71","author":"I Kokkinos","year":"2008","unstructured":"Kokkinos I, Deriche R, Faugeras O et al (2008) Computational analysis and learning for a biologically motivated model of boundary detection. Neurocomputing 71(10\u201312):1798\u20131812","journal-title":"Neurocomputing"},{"key":"4316_CR33","doi-asserted-by":"crossref","unstructured":"Neumann H, Sepp W (1999) Recurrent V1\u2013V2 interaction in early visual boundary processing. Biol Cybern 81:5\u20136","DOI":"10.1007\/s004220050573"},{"issue":"9","key":"4316_CR34","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1109\/TPAMI.2017.2753239","volume":"40","author":"A Akbarinia","year":"2017","unstructured":"Akbarinia A, Parraga CA (2017) Colour constancy beyond the classical receptive field. IEEE Trans Pattern Anal Mach Intell 40(9):2081\u20132094","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4316_CR35","doi-asserted-by":"crossref","unstructured":"Bertasius G, Shi J, Torresani L (2015) Deepedge: a multi-scale bifurcated deep network for top-down contour detection. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Boston,\u00a0pp 4380\u20134389","DOI":"10.1109\/CVPR.2015.7299067"},{"key":"4316_CR36","unstructured":"Shen W, Wang X, Wang Y et al (2015) Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Boston,\u00a0pp 3982\u20133991"},{"issue":"6","key":"4316_CR37","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679\u2013698","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4316_CR38","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Boston,\u00a0pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"4316_CR39","doi-asserted-by":"crossref","unstructured":"Deng R, Shen C, Liu S et al (2018) Learning to predict crisp boundaries. In:\u00a0Proceedings of the European conference on computer vision. Munich,\u00a0pp 562\u2013578","DOI":"10.1007\/978-3-030-01231-1_35"},{"issue":"7","key":"4316_CR40","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2018.2843329","volume":"41","author":"C Cao","year":"2018","unstructured":"Cao C, Huang Y, Yang Y et al (2018) Feedback convolutional neural network for visual localization and segmentation. IEEE Trans Pattern Anal Mach Intell 41(7):1627\u20131640","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4316_CR41","doi-asserted-by":"crossref","unstructured":"Li Z, Yang J, Liu Z et al (2019) Feedback network for image super-resolution. In:\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3867\u20133876","DOI":"10.1109\/CVPR.2019.00399"},{"key":"4316_CR42","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1664\u20131673","DOI":"10.1109\/CVPR.2018.00179"},{"key":"4316_CR43","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2019) Recurrent back-projection network for video super-resolution. In:\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3897\u20133906","DOI":"10.1109\/CVPR.2019.00402"},{"key":"4316_CR44","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1109\/TIP.2019.2940690","volume":"29","author":"Q Tang","year":"2019","unstructured":"Tang Q, Sang N, Liu H (2019) Learning nonclassical receptive field modulation for contour detection. IEEE Trans Image Process 29:1192\u20131203","journal-title":"IEEE Trans Image Process"},{"key":"4316_CR45","doi-asserted-by":"crossref","unstructured":"Zhu X, Yang Z (2013) Multi-scale spatial concatenations of local features in natural scenes and scene classification. Plos one 8(9):e76393","DOI":"10.1371\/journal.pone.0076393"},{"issue":"1","key":"4316_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-04500-5","volume":"9","author":"L Nurminen","year":"2018","unstructured":"Nurminen L, Merlin S, Bijanzadeh M et al (2018) Top-down feedback controls spatial summation and response amplitude in primate visual cortex. Nat Commun 9(1):1\u201313","journal-title":"Nat Commun"},{"key":"4316_CR47","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.conb.2018.05.002","volume":"52","author":"I Choi","year":"2018","unstructured":"Choi I, Lee J-Y, Lee S-H (2018) Bottom-up and top-down modulation of multisensory integration. Curr Opin Neurobiol 52:115\u2013122","journal-title":"Curr Opin Neurobiol"},{"issue":"5","key":"4316_CR48","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/nrn3476","volume":"14","author":"CD Gilbert","year":"2013","unstructured":"Gilbert CD, Li W (2013) Top-down influences on visual processing. Nat Rev Neurosci 14(5):350\u2013363","journal-title":"Nat Rev Neurosci"},{"key":"4316_CR49","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas,\u00a0pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"4316_CR50","doi-asserted-by":"crossref","unstructured":"Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In:\u00a0International workshop on artificial neural networks. Springer,\u00a0Perth,\u00a0pp 195\u2013201","DOI":"10.1007\/3-540-59497-3_175"},{"issue":"1","key":"4316_CR51","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/0006-8993(71)90635-4","volume":"31","author":"M John","year":"1971","unstructured":"John M, Allman et al (1971) A representation of the visual field in the caudal third of the middle temporal gyrus of the owl monkey (Aotus trivirgatus). Brain Res 31(1):85\u2013105","journal-title":"Brain Res"},{"issue":"1","key":"4316_CR52","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1146\/annurev-vision-091517-034202","volume":"4","author":"BR Conway","year":"2018","unstructured":"Conway BR (2018) The organization and operation of inferior temporal cortex. Annual Rev Vis Sci 4(1):381\u2013402","journal-title":"Annual Rev Vis Sci"},{"key":"4316_CR53","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R et al (2009) Imagenet: a large-scale hierarchical image database. In:\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE,\u00a0Miami,\u00a0pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4316_CR54","doi-asserted-by":"crossref","unstructured":"Mottaghi R, Chen X, Liu X et al (2014) The role of context for object detection and semantic segmentation in the wild. In:\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, pp 891\u2013898","DOI":"10.1109\/CVPR.2014.119"},{"key":"4316_CR55","doi-asserted-by":"crossref","unstructured":"Isola P, Zoran D, Krishnan D et al (2014) Crisp boundary detection using pointwise mutual information. In:\u00a0European conference on computer vision. Springer,\u00a0 pp 799\u2013814","DOI":"10.1007\/978-3-319-10578-9_52"},{"key":"4316_CR56","doi-asserted-by":"crossref","unstructured":"Hallman S, Fowlkes CC (2015) Oriented edge forests for boundary detection. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition. Boston, pp 1732\u20131740","DOI":"10.1109\/CVPR.2015.7298782"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04316-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04316-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04316-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:12:58Z","timestamp":1685592778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04316-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,2]]},"references-count":56,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4316"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04316-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,2]]},"assertion":[{"value":"1 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}}]}}